Index-based tools for livelihood security and resilience assessment (LiSeRA) in lower Mekong Basin

This paper presents a framework and toolkit for assessment of multi-hazard livelihood security and resilience in the Lower Mekong Basin (LMB) communities. The LMB is a subsidiary region of the Mekong region in South East Asia, and is frequently exposed to hydrometeorological hazards and anthropogenic stressors that expose and directly affect the livelihoods of more than sixty-five million people living in the region. The main purpose of the study is to support decision-making and risk management planning through integration of the concepts of livelihood security and resilience into a holistic framework, and subsequently developing an index-based toolkit for conducting assessments. Firstly, dimensions, sub-dimensions and indicators for measurement of livelihood security and resilience in the LMB were identified through comprehensive literature review and expert consultation. Then, several local workshops were conducted with various stakeholders (researchers, government officials, community people) in the LMB region to validate the indicators and generate weightages. The indicators were then arranged in a matriculated form, and the weightages were used to generate the algorithm for computing the quantitative outputs of livelihood security and resilience in study area. An Excel toolkit and a ‘R’ programming package were developed using the algorithm for visualization of the assessment outcomes. The proposed framework and toolkit are expected to assist researchers, government officials and development professionals in generating robust resilience assessment indices for risk informed decision-making and planning. Brief outline of the method • Livelihood security and resilience concepts were integrated to generate a holistic assessment framework and an indicator library.• Weightages for indicators were generated using the Analytical Hierarchical Process (AHP) through consultation with relevant stakeholders.• The indicator library was developed into an algorithm-based Excel and ‘R’ programming toolkit that provides quantitative assessment outputs.


Introduction
The Lower Mekong Basin (LMB) is a subsidiary region of the Mekong River, with approximately sixty-five million people directly dependent on the river for livelihood and economic activities [ 1 ]. However, communities in this region are increasingly exposed to multiple hazards that have significant direct and indirect impacts on their livelihoods [2][3][4][5]. In the absence of robust assessment mechanisms, government agencies, development partners and local communities have not been able to design, plan and implement effective actions and strategies for livelihood security and resilience [ 6,7 ]. Thus, the Livelihood Security and Resilience Assessment (LiSeRA) toolkit has been developed to enhance the quality of livelihood resilience assessment in the LMB communities. The toolkit has been developed through a comprehensive review of literature, and inputs from stakeholders including experts, government officials, and local community members who are directly involved in planning and implementing risk reduction strategies and actions [ 8 ]. The toolkit is intended to understand the linkages between livelihood and resilience and conduct quantitative assessments to generate livelihood and resilience indices for different levels (communities, local governments, and state governments).
In the first phase, a comprehensive literature review was done to develop the indicator library for the LiSeRA tool to measure the multi-hazards perspectives of the selected communities in LMB countries (Thailand, Vietnam, and Cambodia). Following the development of the indicator library, capacity-building training workshops were conducted in the project countries for the stakeholder perspectives to define weightage and ranking for the indicators through Analytical Hierarchical Process (AHP). Finally, the resilience indices have been developed through the integration of the indicators based on the LiSeRA framework specified dimensions and sub-dimensions.

Livelihood security and resilience assessment (LiSeRA) framework
The LiSeRA framework consists of two major segments, livelihood dimension and resilience dimension, which are integrated within the governance parameters, well defined by the various legislations, guidelines, laws etc. Livelihood dimensions include five capitals derived from the theoretical principles of sustainable livelihood capitals; Human, Physical, Natural, Financial and Social [ 9 ]. These capitals are determined by a specific vulnerability context; the insecurity of one's livelihood activities and well-being in the face of ecological and environmental changes in the LMB communities. Furthermore, each livelihood capital has been divided into sub-dimensions. Human capital is measured in terms of household members and labor capacity, especially in the face of shortages of labor in the LMB due to migration and changes in trade [ 10,11 ]. Similarly, the physical capacity of LMB communities is measured in terms of the status of infrastructure development and health, measures implemented to increase food production and storage, and availability, accessibility, and quality of commodities and basic services [12] . The natural capitals of the communities include the quality of ecosystem services, such as land and water, and the stock of bio-diversity and environmental resources within the community, which form essential components for the LMB communities considering their significant reliance on their environment for livelihood activities [ 13 ]. Household savings and access to loans and financial support make up the financial capital, providing reserves and buffers in preparation for potential impacts of hazards and stressors [ 11 ]. Finally, the social capital for the community includes formation, participation, and active engagement in community-based organizations, self-help groups, networks, and social support [ 13 ].
On the other hand, community-based vulnerability principles provided the foundations for resilience dimensions, further categorized into three sub-dimensions; Absorb, Response, and Recovery. The LMB communities' capacity to absorb the impacts and stressors primarily relies on their planning and risk reduction strategies, such as flood, and water management infrastructures, advisories and early warning systems, and innovative practices, tools, and technologies in agriculture [ 5 ]. Similarly, the response capacity of communities reflects their ability to manage the impacts of threats and stressors and rely on the capability of the community to react to the changes within their environment [12] . Social bonds and support generated through community partnership and engagement programs, collaboration with government, organizations, and institutions, availability of contingent resources, and knowledge and capacity are key response capacities of the communities [ 14 ]. Finally, the recovery capacity of the LMB communities is measured by their ability to withstand and recover from the impacts of hazards, measured in terms of their institutional functions and capacities, policies, strategies, and technical and financial support for sustainable and disaster-resilient housing and settlements.
The LiSeRA toolkit library has been developed with 58 indicators for resilience and 59 indicators for livelihood security. The resilience indicators are categorized into three dimensions; Absorb, Manage/Respond and Recover, each with four sub-dimensions. Similarly, livelihood security indicators are categorized into five dimensions; human, physical, natural, social, and financial.

Methodology
The flowchart in Fig. 2 shows the methodological approach used in developing the LiSeRA Toolkit. The process was initiated through a comprehensive review of existing literature on the concepts of livelihood security and resilience in river basin communities. Several studies, reports, frameworks and policies were reviewed to understand the linkages between livelihood security and resilience in the LMB communities. These concepts were further synthesized through consultation with experts in the region, and an integrated framework (shown in Fig. 1 ) was developed along with identification of dimensions, sub-dimensions, indicators and the measurement parameters.
In the next stage, several local workshops were conducted with stakeholders including community people, government officials and researchers in the LMB region to discuss the assessment framework and the identified indicators. These discussions were used   to validate the indicators and parameters. Additionally, the stakeholders also conducted pair-wise comparison among the indicators, which was later used to generate their comparative weightages through the Analytical Hierarchical Process (AHP). After the computation of indicator weightage, the algorithm for calculation of livelihood security and resilience indices using the indicators was developed. A six point scale (0-lowest, 5-highest) was developed to normalize the values of quantitative (objective) and qualitative (subjective) data pertinent to the indicators. This algorithm was then used to develop the LiSeRA toolkit, in MS Excel and 'R' programming package that provides the computed assessment indices in tabular and graphical format.

Components of the toolkit Section 1: Dashboard
The LiSeRA Microsoft Excel based toolkit has a dashboard that shows the real-time update of the calculations for livelihood and resilience dimensions indices for the study area based on the inputs given in the toolkit. Two separate dashboards have been prepared.
Dashboard Livelihood ( Table 1 and Fig. 3 ) shows the computed value of livelihood security dimensions (human, social, natural, physical and financial) in the form of matrix table and radar plots. Radar plots show the computed value of the different livelihood security dimensions and the percentage of ideal value.
Dashboard Resilience ( Table 2 and Fig. 4 ) shows the computed values of resilience sub-dimensions (investment, resistance, adaptation, planning, preparedness, capacity, contingency, management, legislature, economy, reconstruction & resilience). The computed resilience indices are displayed in the form of a matrix table ( Table 2 ) as well as radar plots showing the computed values and percentage of ideal value ( Fig. 4 ).

Section 2: Indicator library
The indicator library provides a brief overview of the dimensions, sub-dimensions, and indicators in the livelihood and resilience indices. Along with this, it also displays the weightages assigned to each of the dimensions, sub-dimensions and indicators and allows for modification (if required) during the assessment process.
• Dimension/sub dimension weightage (D Wt./SD Wt.): The weightage for the dimension and/or sub-dimension within the livelihood or resilience index. • Code: The unique code representation for the indicator.
• Indicator: The indicators within each dimension and sub-dimension • Indicator description (measurement): A detailed description of the indicator including the most suitable index of measurement (qualitative or quantitative). • Indicator Wt.: The weightage assigned to the indicators.
• Adjusted Indicator Wt : The adjusted value of indicator weightage is computed automatically by multiplying the dimension, sub-dimension weightages and the indicator weightages.
• References: Sources of journal articles, publications and documents for the respective indicators.

Section 3: Input values
This sheet will be used to input the values for the individual indicators based on the subjective or objective assessment of the study area by the user. Separate sheets have been designed for livelihood and resilience indices. The instructions for placing the input values are shown in the top row of the sheet and described in the following sections. In addition to the fields already described earlier in the indicator library, the input values have the following fields, • Type of indicator: Displays the type of indicator and measurement index (subjective or objective) • Effect on livelihood/resilience: The effect of the indicator on the livelihood or resilience indices (positive indicators will increase livelihood or resilience, and negative will decrease) • Input value: The input value for the particular indicator computed based on the subjective or objective assessment of the study area by the researcher. The input value ranges from 0 (Absent/Very Low) to 5 (Very High). • Indicator Wts: The adjusted weightages for each indicator (refered to from the indicator library sheet).
• Value: The total value of the indicator in the livelihood or resilience index is computed by multiplying the input value with the indicator weightage.
Using the LiSeRA excel toolkit for assessment Step 1: Select and input the dimension, sub-dimension, and indicator weightage for the assessment .
• The weightages for dimensions, sub-dimensions and indicators must be input in the Indicator Library sheets ( Tables 3 & 4 ) • The general instructions for inserting the values in the sheet are placed in the first row.
• The current weightages of the indicators in the LiSeRA toolkit have been generated from pairwise comparison and AHP based on the opinions of experts and stakeholders in the LMB communities. • User may choose to continue your assessment with the same weightages, or generate and enter new weightages based on your own indicator weightage assessment process. • The weightages of the respective dimensions and indicators must be placed in the 'Indicator Library_Livelihood' and 'Indicator Library_Resilience' sheets. • In the 'Indicator Library Livelihood' sheet, the global weightage for five Livelihood dimensions (human, social, natural, physical, and financial) is entered in the column marked 'Dim Wt.'. Similarly, indicator weightages is entered in the column marked 'Ind. Wt.'. It must be ensured that the sum of weightages for five dimensions and twelve indicators in each dimension equals to 1 (One). • The similar process must be done to enter the weightages of the dimension and indicators in the resilience dimensions, in the "Indicator Library_Resilience " sheet.

Step 2: Input values for livelihood and resilience dimension indicators
• The values for indicators within the livelihood and resilience dimension must be placed in the 'Input Values_Livelihood' and 'Input Values_Resilience' sheets respectively ( Tables 5 & 6 ) • The general instructions for inserting the values in the sheet are placed in the first row.
• Before inserting the values for the indicators, measurement index (description of indicator including the metric of measurement), the type of indicator (subjective or objective), and the effect on livelihood or resilience (positive or negative) must be checked carefully.
• Input values must be accurately chosen based on the type of indicator and the effect of the indicator on the livelihood and resilience dimensions as follows, ○ For objective indicators, a normalization scale must be developed to categorize data obtained for the study area into six levels: 0 -lowest, 5 -highest. ○ For subjective indicators, subjective assessment must be conducted in the study area. The information collected must be categorized into six categories and respective input values as shown in the Table 7 . • In the case of both positive and negative indicators, the input data will follow the same scaling. For both indicators, only whole numbers should be entered, without any signs (-/ + ). The toolkit has been calibrated to automatically assign negative values to the negative indicators.         • After determining the values for the indicators, the input values must be placed in the respective columns; Column G in 'Input Values_Livelihood' sheet ( Table 5 ) and Column H in the 'Input Values_Resilience' sheet ( Table 6 ). • Once the values for the indicators are placed in the respective fields, the adjusted values are automatically calculated by multiplying the input value with the indicator weightage.

Step 3: Check tabular and graphical outputs
• The outputs of the livelihood security and resilience will be generated in the LiSeRA dashboard.
• The outputs will be generated in tabular (matrix) and graphical form through radar plots (see Tables 1 , 2  • Sum: The total sum for dimension (or sub dimension) for livelihood and resilience is shown.
• % of ideal value: The percentage of sum of livelihood or resilience dimensions and sub-dimensions with respect to the ideal value (generated through ideal inputs)

Conclusion
The LiSeRA toolkit has been developed through a comprehensive review of literature, and inputs from stakeholders including experts, government officials, and local community members who are directly involved in planning and implementing risk reduction strategies and actions. The toolkit is intended for researchers, development planners, and policymakers to understand the linkages between livelihood and resilience and conduct quantitative assessments to generate livelihood and resilience indices for different levels (communities, local governments, and state governments). Additionally, the toolkit also serves as a reference for researchers and practitioners in identifying critical indicators that can be used to plan and evaluate risk assessment and management actions within the LMB communities. Livelihood security encompasses the people's ability to cope with natural and anthropogenic hazards and threats such as irregular weather patterns, resource constraints, epidemics and diseases, and market fluctuations that contribute to the sustainability of their livelihoods. Future research should focus on developing well-informed resilience status maps, which include the interdependency parameters of various infrastructures, such as hydrological infrastructures that support livelihood (e.g., dams, water gates, irrigation canals, freshwater ponds, water reservoirs etc.), electricity, transit as well as interactions between other systems. Finally, the LiSeRA framework focuses on assessing livelihood security in the river basin system; however, its application can be extended to a wide range of livelihoods and other geophysical settings.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability
Data will be made available on request.